Defining and Predicting Honeybee Colony Health Levels through Clustering and Classification
Abstract
Bees are essential for the production of food for humans and the maintenance of ecosystems. This paper presents a solution for calculating bee colony health status levels using data from internal and external colony sensors and onsite inspections by beekeepers. Clustering was used to determine the number of health levels and classification to create a prediction model. We obtained a classification model with an accuracy ratio of 99.36%.
Keywords:
Precision beekeeping, Apis mellifera, Bee Health Monitoring, Machine Learning
References
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Calinski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1):1–27. DOI: https://doi.org/10.1080/03610927408827101
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, pages 281–297. Oakland, CA, USA.
Potts, S., Biesmeijer, J., Kremen, C., Neumann, P., Schweiger, O., and Kunin, W. (2010). Global pollinator declines: Trends, impacts and drivers. Trends in ecology & evolution, 25:345–53. DOI: https://doi.org/10.1016/j.tree.2010.01.007
Potts, S. G. et al. (2016). Safeguarding pollinators and their values to human well-being. Nature, 540:220–229. DOI: https://doi.org/10.1038/nature20588
Published
2019-10-07
How to Cite
BRAGA, Antonio Rafael; SILVA, Daniel A.; NOBRE, Juvêncio S.; FREITAS, Breno M.; GOMES, Danielo G..
Defining and Predicting Honeybee Colony Health Levels through Clustering and Classification. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 34. , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 241-246.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2019.8830.
